What is the correct sklearn syntax to add a third degree polynomial to your model? 1 pointpolyFeat = polyFeat.add(degree=3) polyFeat = polyFeat.fit(degree=3)polyFeat = PolynomialFeatures(degree=3)polyFeat = polyFeat.transform(degree=3)

Problem statementSend feedbackWhich of the following class of the sklearn library can be used to convert the independent features of a dataset into various degrees of polynomial features?

You are refining a logistic regression model to predict customer churn. The dataset includes various customer interaction metrics. To enhance your model, explore how polynomial features can improve prediction accuracy. This approach allows the model to capture complex interactions between variables.Here is your setup:from sklearn.datasets import make_classificationfrom sklearn.model_selection import train_test_splitfrom sklearn.linear_model import LogisticRegressionfrom sklearn.preprocessing import PolynomialFeatures# Generate synthetic data for binary classificationX, y = make_classification(n_samples=1000, n_features=3, n_classes=2, random_state=42)# Split the data into training and testing setsX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)# Apply polynomial features manuallypoly = PolynomialFeatures(degree=2)X_train_poly = poly.fit_transform(X_train)X_test_poly = poly.transform(X_test)What is the correct procedure to fit a logistic regression model on the training data after transforming it with polynomial features, and how should predictions be made on the test data?model = LogisticRegression()model.fit(X_train, y_train)y_pred = model.predict(X_test)model = LogisticRegression()model.fit(X_test_poly, y_test)y_pred = model.predict(X_train_poly)model = LogisticRegression()model.fit(X_train_poly, y_train)y_pred = model.predict(X_test_poly)model = LogisticRegression()model.fit(X_train_poly, y_test)y_pred = model.predict(X_test_poly)

>>polyfit (X, Y, 3) creates and evaluates a polynomial of order 3 where X and Y are arrays of the same length

Traceback (most recent call last): File "/tmp/005714092/main.py", line 13, in <module> data['Height'] = imputer.fit_transform(data['Height'].values.reshape(-1,1)) File "/usr/local/lib/python3.9/site-packages/sklearn/utils/_set_output.py", line 157, in wrapped data_to_wrap = f(self, X, *args, **kwargs) File "/usr/local/lib/python3.9/site-packages/sklearn/base.py", line 916, in fit_transform return self.fit(X, **fit_params).transform(X) File "/usr/local/lib/python3.9/site-packages/sklearn/base.py", line 1145, in wrapper estimator._validate_params() File "/usr/local/lib/python3.9/site-packages/sklearn/base.py", line 638, in _validate_params validate_parameter_constraints( File "/usr/local/lib/python3.9/site-packages/sklearn/utils/_param_validation.py", line 95, in validate_parameter_constraints raise InvalidParameterError(sklearn.utils._param_validation.InvalidParameterError: The 'metric' parameter of KNNImputer must be a str among {'nan_euclidean'} or a callable. Got 'nan_enclidean' instead.

Find the degree of (x3 − x2)2